Predictive maintenance course for turbines
Online course of 300 hours in predictive maintenance for turbines: 6 modules covering engine maintenance strategies, data for predictive maintenance, trend analysis and diagnosis, prognosis and remaining useful life, implementation in the organisation, and a predictive maintenance project, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand engine maintenance strategies and the role of predictive maintenance.
Prepare and correct operational and maintenance data.
Apply trend analysis and gas path analysis for diagnosis.
Develop remaining useful life prognosis models.
Evaluate model uncertainty and performance.
Integrate predictive maintenance into the organisation and its maintenance programme.
Deliver a predictive maintenance project using real data.
Who it's for
- Predictive maintenance engineers focusing on gas turbines.
- Aviation data scientists.
- Engine reliability engineers.
- Maintenance planning engineers who want to use condition data in their decisions.
- Engine health monitoring analysts.
- Fleet technical support engineers.
- Engineering or science graduates looking to join airlines.
What you'll work with
- ISO 13381-1:2015 (condition monitoring and diagnostics – prognostics)
- ISO 13374-1:2003 (data processing, communication and presentation for condition monitoring)
- ISO 17359:2018 (condition monitoring and diagnostics of machines – general guidelines)
- SAE ARP1587B (aircraft gas turbine engine health management system guide)
- Regulation (EU) No 1321/2014 (continuing airworthiness)
- FAA AC 120-17B (reliability programme methods)
- ATA MSG-3 (maintenance programme development)
- ATA Spec 2000 (e-business specification for aviation maintenance data)
- Python with scikit-learn
- MATLAB Predictive Maintenance Toolbox
- Azure Machine Learning
- ReliaSoft Weibull++
- Microsoft Power BI
6 modules
1.1Corrective, preventive and condition-based maintenance1.2MSG-3 philosophy applied to engines1.3Operator reliability programmes1.4Life-limited parts and their control1.5Shop visits and associated costs1.6Power-by-the-hour contracts1.7Reliability indicators: IFSD and removals1.8From diagnosis to prognosis1.9Benefits of predictive maintenance1.10Case study: costs of an unscheduled engine removal
2.1Cruise and take-off performance parameters2.2ACMS snapshots and continuous data2.3Vibration and oil debris data2.4Digitised borescope inspections2.5Maintenance and component records2.6Environmental and operational data2.7Data quality and cleansing2.8Correction to standard conditions2.9Storage architectures2.10Workshop: cleansing and correcting EGT trends
3.1EGT margins and their deterioration3.2Gas path analysis3.3Influence coefficient matrices3.4Step-change detection3.5Oil debris and spectrometric analysis3.6Compressor wash diagnosis3.7Correlation between parameters3.8Known failure signatures3.9Alert reports to the operator3.10Lab: gas path analysis of a turbofan
4.1Physics-based degradation models4.2Data-driven models4.3Regression, survival and Weibull analysis4.4Recurrent neural networks4.5Remaining useful life estimation4.6Uncertainty in life prediction4.7Model validation with historical data4.8The C-MAPSS dataset4.9Prognostic metrics4.10Practical exercise: remaining useful life prediction with C-MAPSS
5.1Integration with the maintenance programme5.2Regulatory approval of task changes5.3Shop visit planning5.4Spare parts and spare engine management5.5OEM and third-party platforms5.6Cybersecurity and data ownership5.7Return on investment5.8Change management in technical teams5.9Airline success stories5.10Case study: business case for twenty engines
6.1Selected fleet and problem6.2Dataset preparation6.3Exploratory analysis and trends6.4Development of the diagnostic model6.5Life prognosis model6.6Evaluation of model performance6.7Proposal for operational integration6.8Economic analysis of benefits6.9Report and presentation6.10Final project: final review with the tutor
How you'll learn
A 100% online programme on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with scikit-learn, MATLAB Predictive Maintenance Toolbox, Azure Machine Learning, ReliaSoft Weibull++ and Microsoft Power BI, in free, academic or evaluation versions. Testing is studied through real procedures for EGT margin trend analysis, remaining useful life prediction with C-MAPSS data and building maintenance business cases. Assessment is continuous by module, plus the final project.
Standards covered: ISO 13381-1:2015, ISO 13374-1:2003, ISO 17359:2018, SAE ARP1587B, Regulation (EU) No 1321/2014, FAA AC 120-17B, ATA MSG-3, ATA Spec 2000
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python with scikit-learn, MATLAB Predictive Maintenance Toolbox, Azure Machine Learning, ReliaSoft Weibull++, Microsoft Power BI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No prior qualification is required: you need an internet connection, a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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